ספקית עליונה מאיטה את הדריפט הרפלקטיבי יותר מאשר כובשת אותו

תקציר מקורי באנגליתarXiv:2609.11146v3 Announce Type: replace-cross Abstract: More and more of the text future language models learn from is written by a few of today's models. If one supplier writes most of a shared corpus, does it pull the models trained on it toward its own writing, or change how fast they drift? We retrain eight open models from their base weights on a shared pool of each other's text for five generations, varying the part written by one model, Phi-2, from an equal share to 90%. The models drift together toward a style with fewer function words, and none starts repeating itself. No share of Phi-2 brings the other models closer to its text than the equal share does. We split each ecosystem's separation from the equal-share one into a delay along its route and a departure from that route, b